It is oftentimes desirable to compress data. For example, it may be desirable to compress data that has been collected by a collection platform prior to transmission of the data to a ground station or the like. Further, it may be desirable to compress data prior to archiving the data and/or prior to further disseminating the data.
For example, hyperspectral imagery data may be voluminous and, as such, is desirably compressed prior to being transmitted, stored or otherwise disseminated. In this regard, hyperspectral imaging collects a set of image planes, such as aerial views of a selected geographic area, sampled in narrow spectral bands covering one or more contiguous spectral intervals. Narrow spectral bands allow spectroscopic methods to be combined with image analysis techniques in exploitation of the remotely sensed image data. Each spectral band within a hyperspectral image collection is a complete spatial image, albeit confined to a narrow spectral band. These individual spectral bands are also sometimes referred to as spectral components or spectral planes.
A complete hyperspectral image collected over a particular area is commonly called a hyperspectral cube, and can be characterized by the dimensions S×M×N×D, where S is the number of spectral bands, M and N are the spatial dimensions, and D is the pixel depth, such as in bits per pixel. Hyperspectral images typically comprise tens to hundreds of spectral bands. For example, NASA's Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) program collected 224 spectral bands at 10 nm band intervals, with typical cube dimensions of 224×614×512×16.
The use of hyperspectral imagery is becoming increasingly popular due to the introduction of new platforms for hyperspectral image collection and to improvements in computer speed and memory for handling hyperspectral data. However, the large amounts of data associated with hyperspectral imagery, e.g., 134 Mbytes for a single AVIRIS cube, pose challenges to data storage and transmission systems. Efficient compression of hyperspectral imagery is therefore of importance in order to manage transmission and storage requirements.
Hyperspectral image compression may be useful in at least two situations. Firstly, since the hyperspectral image is generally collected with an airborne platform, downlink compression must be applied on the collection platform, so that the imagery may be more efficiently transmitted to a ground station via a downlink. Downlink compression faces at least two constraints: (a) computation is constrained due to limits on size, weight, power, and execution time on the collection platforms, and (b) compression must be lossless or nearly lossless so that little or none of the collected information is lost prior to ground processing and archiving. Due to these constraints, commercial and standard image compression methods may not be readily applicable to downlink compression.
Secondly, compression may be advantageously applied at a ground station. After downlink, hyperspectral imagery is oftentimes decompressed and processed for operations such as atmospheric compensation and correction of “dead pixels”. Secondary compression may then be performed so that the imagery may be more efficiently archived and/or disseminated. Secondary compression of hyperspectral imagery may be in compliance with the JPEG 2000 standard, which is being adopted by the National Imagery and Mapping Agency (NIMA). The multicomponent compression facilities defined in Part 2 of JPEG 2000 provide a flexible framework for the compression of hyperspectral imagery. This framework can support a variety of approaches for decorrelating the spectral bands, including a spectral wavelet transform, the Karhunen-Loeve (K-L) transform, simple linear prediction, and other linear transforms.
In order to compress hyperspectral imagery data, the spectral bands may initially be decorrelated based on, for example, predictive coding and/or transform coding, such as by reliance upon the K-L transform or the wavelet transform. More recently, spectral decorrelation methods may be utilized that are locally adapted, as opposed to a prediction or transform that is applied uniformly and globally across the spatial extent of the hyperspectral image. For example, a decorrelating predictor may be modified using local spatial adaptation. Alternatively, spectrally classified predictors may be utilized in which clustering algorithms are utilized to classify pixels into groups according to their spectral signature with separate adaptive linear predictors then used for each class. By exploiting the nonstationarity of the data, these more locally adapted decorrelation methods may offer improved performance relative to classical approaches that utilize stationary statistics. However, the decorrelation techniques that utilize spectrally classified predictors may suffer from increased computational and processing costs as a result of the effort to classify the pixels into spectral classes. Indeed, the classification must generally be performed off-line and typically requires one or more additional passes through the voluminous hyperspectral imaging data.
The decorrelated spectral bands may then be encoded utilizing, for example, a conventional image compression method. In this regard, for lossless compression, predictive coding methods, such as JPEG-LS, may be utilized to compress the spectral bands, while for lossy compression, wavelet methods, such as those defined by JPEG 2000, or other methods, may be implemented to compress the spectral bands.
In light of the issues associated with decorrelation, it would be desirable for providing an improved technique for compressing data, such as hyperspectral imaging data. In this regard, it would be desirable to provide an improved technique for decorrelating data, such as hyperspectral imaging data, such that the decorrelation can be performed more efficiently with lower computational and processing costs than at least some conventional approaches.